In recent years, there has been tremendous amount of work on real-time vision-based tracking and mapping (T&M) systems. That is, systems that simultaneously determine one or both of the position and orientation of a camera with respect to a previously unknown environment and creation of a model of the viewed environment. In addition to other applications, T&M is an enabling technology for Augmented Reality (AR) in unprepared environments.
Two characteristics of a T&M system are the type of camera motion and the geometry of the environment that it supports. For example, a system may assume a planar environment or a camera that is rotating around its optical center, i.e., a panoramic capture. Simultaneous Localization and Mapping (SLAM) systems handle environments of arbitrary geometry and camera motion that induces parallax. However, SLAM systems generally do not support rotation-only camera motion. Instead, in SLAM systems the mapping is intrinsically built upon triangulation of features where each feature needs to be observed from two distinct camera locations; they produce degenerate maps or fail completely when the camera rotates from one part of the environment to another.
Therefore, most SLAM systems need to be initialized with a distinct “traveling” movement of the camera for each newly observed part of the environment, and the required travel distance is directly proportional to the distance to the environment and features therein.